Abstract
Patients discuss complementary and alternative medicine (CAM) in online health communities. Sometimes, patients’ conflicting opinions toward CAM-related issues trigger debates in the community. The objectives of this paper are to identify such debates, identify controversial CAM therapies in a popular online breast cancer community, as well as patients’ stances towards them. To scale our analysis, we trained a set of classifiers. We first constructed a supervised classifier based on a long short-term memory neural network (LSTM) stacked over a convolutional neural network (CNN) to detect automatically CAM-related debates from a popular breast cancer forum. Members’ stances in these debates were also identified by a CNN-based classifier. Finally, posts automatically flagged as debates by the classifier were analyzed to explore which specific CAM therapies trigger debates more often than others. Our methods are able to detect CAM debates with F score of 77%, and identify stances with F score of 70%. The debate classifier identified about 1/6 of all CAM-related posts as debate. About 60% of CAM-related debate posts represent the supportive stance toward CAM usage. Qualitative analysis shows that some specific therapies, such as Gerson therapy and usage of laetrile, trigger debates frequently among members of the breast cancer community. This study demonstrates that neural networks can effectively locate debates on usage and effectiveness of controversial CAM therapies, and can help make sense of patients’ opinions on such issues under dispute. As to CAM for breast cancer, perceptions of their effectiveness vary among patients. Many of the specific therapies trigger debates frequently and are worth more exploration in future work.
Highlights
Complementary and alternative medicine (CAM) is increasingly used by populations worldwide in concert with conventional, evidence-based medicine, for treating and managing chronic diseases and life-threatening illnesses [4, 18, 40, 27]
We train two models to solve these two problems with the manual labels as we describe in Section 2.2 by minimizing the negative log-likelihood (NLL) of the post sequence: (5)
To get an evidence of why long short-term memory neural network (LSTM)+convolutional neural network (CNN) works better for debate detection, we looked at weights assigned by logistic regression model after training, and found that W2V-sim, NumName, NumOverlap were the top three features associated, either positively or negatively, with debate identification, while NAgree, NumPos, and NumCAM were most correlated with stances
Summary
Complementary and alternative medicine (CAM) is increasingly used by populations worldwide in concert with conventional, evidence-based medicine, for treating and managing chronic diseases and life-threatening illnesses [4, 18, 40, 27]. Patients may take CAM following personal beliefs, sometimes without informing their care providers [12]. For healthcare practitioners and researchers, it is critical to gain a deeper insight into how CAM therapies are perceived and used by patients. Recent research has focused on attitudes of physicians and patients toward CAM relying on different study instruments, many of which found incongruent views on effectiveness [12, 24, 2, 28]. Most of these studies are based on rigorous study designs on sampled populations, in which subjects are asked to respond to survey instruments or participate in focus groups
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